Machine Learning Skills you will learn

  • Supervised and unsupervised learning
  • Time series modeling
  • Linear and logistic regression
  • Kernel SVM
  • KMeans clustering
  • Naive Bayes
  • Decision tree
  • Random forest classifiers
  • Boosting and Bagging techniques
  • Deep Learning fundamentals

Who should learn Machine Learning

  • Analytics Managers
  • Business Analysts
  • Information Architects
  • Developers

What you will learn in Machine Learning Basics Program

  • Machine Learning

    • Lesson 01: Course Introduction

      09:19
      • 1.01 Course Introduction
        06:08
      • 1.02 Demo: Jupyter Lab Walk - Through
        03:11
    • Lesson 02: Introduction to Machine Learning

      08:40
      • 2.01 Learning Objectives
        00:42
      • 2.02 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A
        02:46
      • 2.03 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B
        01:23
      • 2.04 Definition and Features of Machine Learning
        01:30
      • 2.05 Machine Learning Approaches
        01:46
      • 2.06 Key Takeaways
        00:33
    • Lesson 03: Supervised Learning Regression and Classification

      02:10:59
      • 3.01 Learning Objectives
        00:46
      • 3.02 Supervised Learning
        02:18
      • 3.03 Supervised Learning: Real Life Scenario
        00:55
      • 3.04 Understanding the Algorithm
        00:54
      • 3.05 Supervised Learning Flow
        01:51
      • 3.06 Types of Supervised Learning: Part A
        01:57
      • 3.07 Types of Supervised Learning: Part B
        02:05
      • 3.08 Types of Classification Algorithms
        01:03
      • 3.09 Types of Regression Algorithms: Part A
        03:23
      • 3.10 Regression Use Case
        00:36
      • 3.11 Accuracy Metrics
        01:24
      • 3.12 Cost Function
        01:49
      • 3.13 Evaluating Coefficients
        00:55
      • 3.14 Demo: Linear Regression
        13:48
      • 3.15 Challenges in Prediction
        01:47
      • 3.16 Types of Regression Algorithms: Part B
        02:40
      • 3.17 Demo: Bigmart
        37:29
      • 3.18 Logistic Regression: Part A
        02:01
      • 3.19 Logistic Regression: Part B
        01:41
      • 3.20 Sigmoid Probability
        02:07
      • 3.21 Accuracy Matrix
        01:28
      • 3.22 Demo: Survival of Titanic Passengers
        13:17
      • 3.23 Overview of Classification
        02:03
      • 3.24 Classification: A Supervised Learning Algorithm
        00:52
      • 3.25 Use Cases
        02:34
      • 3.26 Classification Algorithms
        00:17
      • 3.27 Performance Measures: Confusion Matrix
        02:21
      • 3.28 Performance Measures: Cost Matrix
        02:07
      • 3.29 Naive Bayes Classifier
        01:16
      • 3.30 Steps to Calculate Posterior Probability: Part A
        01:41
      • 3.31 Steps to Calculate Posterior Probability: Part B
        02:22
      • 3.32 Support Vector Machines: Linear Separability
        01:05
      • 3.33 Support Vector Machines: Classification Margin
        02:06
      • 3.34 Linear SVM: Mathematical Representation
        02:05
      • 3.35 Non linear SVMs
        01:07
      • 3.36 The Kernel Trick
        01:19
      • 3.37 Demo: Voice Classification
        10:42
      • 3.38 Key Takeaways
        00:48
    • Lesson 04: Decision Trees and Random Forest

      18:09
      • 4.01 Learning Objectives
        00:37
      • 4.02 Decision Tree: Classifier
        02:17
      • 4.03 Decision Tree: Examples
        01:44
      • 4.04 Decision Tree: Formation
        00:46
      • 4.05 Choosing the Classifier
        02:56
      • 4.06 Overfitting of Decision Trees
        01:01
      • 4.07 Random Forest Classifier Bagging and Bootstrapping
        02:19
      • 4.08 Decision Tree and Random Forest Classifier
        01:07
      • 4.09 Demo: Horse Survival
        04:57
      • 4.10 Key Takeaways
        00:25
    • Lesson 05: Unsupervised Learning

      32:41
      • 5.01 Learning Objectives
        00:36
      • 5.02 Overview
        01:47
      • 5.03 Example and Applications of Unsupervised Learning
        02:17
      • 5.04 Clustering
        01:46
      • 5.05 Hierarchical Clustering
        02:30
      • 5.06 Hierarchical Clustering: Example
        02:02
      • 5.07 Demo: Clustering Animals
        05:40
      • 5.08 K-means Clustering
        03:54
      • 5.09 Optimal Number of Clusters
        03:27
      • 5.10 Demo: Cluster Based Incentivization
        08:18
      • 5.11 Key Takeaways
        00:24
    • Lesson 06: Time Series Modelling

      38:57
      • 6.01 Learning Objectives
        00:24
      • 6.02 Overview of Time Series Modeling
        02:16
      • 6.03 Time Series Pattern Types: Part A
        02:16
      • 6.04 Time Series Pattern Types: Part B
        01:19
      • 6.05 White Noise
        01:06
      • 6.06 Stationarity
        02:13
      • 6.07 Removal of Non Stationarity
        02:13
      • 6.08 Demo: Air Passengers I
        14:26
      • 6.09 Time Series Models: Part A
        02:14
      • 6.10 Time Series Models: Part B
        01:28
      • 6.11 Time Series Models: Part C
        01:51
      • 6.12 Steps in Time Series Forecasting
        00:37
      • 6.13 Demo: Air Passengers II
        06:14
      • 6.14 Key Takeaways
        00:20
    • Lesson 07: Ensemble Learning

      39:35
      • 7.01 Learning Objectives
        00:24
      • 7.02 Overview
        02:41
      • 7.03 Ensemble Learning Methods: Part A
        02:49
      • 7.04 Ensemble Learning Methods: Part B
        04:09
      • 7.05 Working of AdaBoost
        01:43
      • 7.06 AdaBoost Algorithm and Flowchart
        02:28
      • 7.07 Gradient Boosting
        04:37
      • 7.08 XGBoost
        02:23
      • 7.09 XGBoost Parameters: Part A
        03:15
      • 7.10 XGBoost Parameters: Part B
        02:30
      • 7.11 Demo: Pima Indians Diabetes
        03:11
      • 7.12 Model Selection
        02:55
      • 7.13 Common Splitting Strategies
        01:45
      • 7.14 Demo: Cross Validation
        04:18
      • 7.15 Key Takeaways
        00:27
    • Lesson 08: Recommender Systems

      26:11
      • 8.01 Learning Objectives
        00:27
      • 8.02 Introduction
        02:16
      • 8.03 Purposes of Recommender Systems
        00:45
      • 8.04 Paradigms of Recommender Systems
        02:45
      • 8.05 Collaborative Filtering: Part A
        02:14
      • 8.06 Collaborative Filtering: Part B
        01:58
      • 8.07 Association Rule: Mining
        01:47
      • 8.08 Association Rule: Mining Market Basket Analysis
        01:42
      • 8.09 Association Rule: Generation Apriori Algorithm
        00:53
      • 8.10 Apriori Algorithm Example: Part A
        02:13
      • 8.11 Apriori Algorithm Example: Part B
        01:17
      • 8.12 Apriori Algorithm: Rule Selection
        02:52
      • 8.13 Demo: User Movie Recommendation Model
        04:12
      • 8.14 Key Takeaways
        00:50
    • Lesson 09: Level Up Sessions

      10:31
      • Session 01
        05:22
      • Session 02
        05:09
    • Practice Project

      • California Housing Price Prediction
      • Phishing Detector with LR

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Learn the Basics of Machine Learning

Why you should learn Machine Learning

$8.81 billion

Expected machine learning market growth by 2022

44.1% growth

In the adoption of machine learning in organizations

Career Opportunities

FAQs

  • What are the prerequisites to learn the Machine Learning basics program?

    Prior knowledge of basic mathematics, statistics, and Python programming is beneficial to take this machine learning basics course.

  • How do beginners learn Machine Learning basics?

    Beginners often rely on free online tutorials or to learn the fundamentals of Machine Learning. For a reliable start in this field, Simplilearn’s free Machine Learning for beginners course is an excellent option and then you can move on to our AI and ML Course.

  • How long does it take to learn Machine Learning?

    The time required to learn machine learning varies for every learner depending on their educational background and prior exposure in the field. The 7 hours of online content covered in this course will surely help you get the ML basics right in a short amount of time.

  • What should I learn first in the Machine Learning basics program?

    Professionals who wish to start with Machine Learning first get a complete overview of Artificial Intelligence, its applications and how various industries are using it. Then they learn about Machine Learning, its approaches and techniques.

  • Is the Machine Learning foundations program easy to learn?

    Simplilearn curates all of its courses as per the learners’ needs. Even if you don’t have any prior idea of Machine Learning, it will be easy for you to follow the video lessons covered in this Machine Learning fundamentals program.

  • What are the basics in a Machine Learning foundations training program?

    Simplilearn’s Machine Learning free online course starts with the basics like how Machine Learning is related to artificial intelligence, common terminologies in this field, and types of Machine Learning - supervised, unsupervised, and reinforced.

  • What is Machine Learning?

    Machine Learning is an integral branch of Artificial Intelligence (AI). Applications in ML learn from experience like humans — in this case, from data — without any direct programming. Every time Machine Learning applications are exposed to new data, they learn and retrain themselves by leveraging algorithms in an iterative process.

  • What is Machine Learning used for?

    Machine Learning is used to make systems capable of learning on their own and improving their actions by taking feedback from past experiences, just like humans do. As Machine Learning facilitates the analysis of huge amounts of data, companies use it to get faster and more accurate results and earn greater profits.

  • Why learn Machine Learning?

    Machine Learning (ML) is the technology that has revolutionized the way we live in the 21st century.  Self-driving cars, cyber fraud detection, and online recommendation engines from Facebook, Spotify, Netflix, and Amazon are all applications of machine learning. According to a recent report from TMR, MLaaS (Machine learning as a Service) is expected to grow to $19.9 billion by the end of 2025. Almost every customer-centric organization today is en route to AI adoption in some form or another. This has led to a simultaneous surge in the demand for trained machine learning engineers across top enterprises worldwide — meaning that now is the time to learn by enrolling in machine learning courses. For more information, watch this video.

  • Who can learn Machine Learning?

    You can learn Machine Learning if you are one of the following professionals:

    • Analytics Manager
    • Business Analyst
    • Information Architect
    • Developer

  • Can I complete this Machine Learning foundations program in 90 days?

    Following at your own pace, you can comfortably complete the course within 90 days.

  • Will I get a certificate after completing the Machine Learning basics program?

    Yes, You will receive a Course Completion Certificate from SkillUp upon completing the Machine Learning basics program. You can unlock it by logging in to your SkillUp account. As soon as the certificate is unlocked, you will receive a mail with a link to your SkillUp learning dashboard on your registered mail address. Click the link to view and download your certificate. You can even add the certificate to your resume and share it on social media platforms.

  • What are my next best learning options after completing this Machine Learning basics course?

    After completing this training on Machine Learning basics, you can learn advanced concepts with other courses like Artificial Intelligence Course, Master in Artificial Intelligence or PG Program in AI and Machine Learning.

  • What are the career opportunities in Machine Learning?

    The knowledge of Machine Learning comes in handy for many evolving job roles like data scientist, data analyst, Machine Learning engineer or AI engineer. We are just in the initial stages of AI adoption and the future looks promising for Machine Learning professionals. Many companies are seeking skilled candidates in this domain for their AI-powered projects.

Learner Review

  • M Ehsani

    M Ehsani

    Thanks to Simplilearn for providing such an insightful course on Machine Learning. Looking forward to applying my learnings in a real-world project.

  • Jyoti Dange

    Jyoti Dange

    The course was really good. I am thorough with the fundamentals of Machine Learning and I have recommended this course to my friends.

  • Rajeev Gaur

    Rajeev Gaur

    The course gave me a lot of exposure to the practical side of Machine Learning projects. It was an awesome experience.

  • Daren Lee

    Daren Lee

    The course material covered concepts with clarity through real-life examples.

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  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.